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Design and development of AC motor speed controlling system using touch screen with over heat protection

10.11591/ijpeds.v16.i4.pp2429-2440
Prathipati Ratna Sudha Rani , Gouthami Eragamreddy , Syed Inthiyaz , Sivangi Ravikanth , Mohammad Najumunnisa , Bodapati Venkata Rajanna , Cheeli Ashok Kumar , Shaik Hasane Ahammad
Design and implementation of an AC motor speed control and monitoring system based on a touch screen interface with built-in overheat protection, utilizing Arduino, meets the increasing demand for efficient, user-friendly motor control in many industrial applications. This system offers an easy-to-use interface to manage the speed of an AC motor, with real-time feedback and adjustments through a touch screen display. The system employs an Arduino microcontroller, which accepts inputs from the touch screen and processes these to regulate the motor's speed through a pulse width modulation (PWM) method. The system also has an overheat protection system, which it is able to monitor the temperature of the motor via a temperature sensor. When the motor reaches a predetermined temperature, the system automatically shuts off power to avoid damage. The intuitive touch screen facilitates convenient monitoring of motor parameters like temperature, giving a smooth experience to operators. The modular design of the system provides scalability across applications, ranging from household appliances to large industrial systems, with reliability, energy efficiency, and safety in motor-driven processes.
Volume: 16
Issue: 4
Page: 2429-2440
Publish at: 2025-12-01

Machine learning model for accurate prediction of coronary artery disease by incorporating error reduction methodologies

10.11591/ijece.v15i6.pp5655-5666
Santhosh Gupta Dogiparthi , Jayanthi K. , Ajith Ananthakrishna Pillai , K. Nakkeeran
Coronary artery disease (CAD) remains a leading cause of mortality worldwide, with an especially high burden in developing countries such as India. In light of increasing patient loads and limited medical resources, there is an urgent need for accurate and reliable diagnostic support systems. This study introduces a machine learning (ML) framework that aims to enhance CAD prediction accuracy by specifically addressing the reduction of false negatives (FN), which are critical in medical diagnostics. Utilizing a stacked ensemble model comprising five base classifiers and a meta-classifier, the framework integrates cost-sensitive learning, classification threshold tuning, engineered features, and manual weighting strategies. The model was developed using a clinically acquired dataset from the Jawaharlal Institute of postgraduate medical education and research (JIPMER), consisting of 428 patient records with 36 original features. Evaluation metrics show that the proposed model achieved an accuracy of 92.19%, sensitivity of 98%, and an F1-score of 95.15%. These improvements are significant in a clinical context, potentially reducing missed diagnoses and improving patient outcomes. The model is intended for deployment in cardiology outpatient settings and demonstrates a scalable, adaptable approach to medical diagnostics.
Volume: 15
Issue: 6
Page: 5655-5666
Publish at: 2025-12-01

Platforma: a modular and agile framework for simplified platformer game development

10.11591/ijece.v15i6.pp5535-5542
Rickman Roedavan , Abdullah Pirus Leman , Bambang Pudjoatmodjo
Research on game development frameworks has been extensively conducted; however, most frameworks are still too general. Conventional game frameworks are challenging for students who are new to game development, especially with their limited information and skills. Beginner game developers should ideally be guided by a practical and specific framework to help them better understand the structure of game development in a more directed manner. This paper proposes platformer modular and agile framework (Platforma) that specifically designed for platformer game development. The framework is built based on the atomic design model, breaking down each minor feature of a platformer game element and grouping these features into more specific modules. The framework was tested on three teams of students. Each team was tasked with developing a platformer game with a minimum of 15 levels of the reach game goals typology. Testing results involving 100 respondents using the game experience questionnaire (GEQ) indicated that the games developed had a positive aspect score of 3.48 and a negative aspect score of 2.65. Overall, these results suggest that the Platforma can serve as an effective guide for beginners in developing platformer games.
Volume: 15
Issue: 6
Page: 5535-5542
Publish at: 2025-12-01

Adaptive control strategies for enhancing the performance and stability of renewable energy systems

10.11591/ijpeds.v16.i4.pp2411-2418
Sreedevi Kunumalla , Durgam Rajababu , A. V. V. Sudhakar
The comparison done in this paper uses two control strategies for a solar-fed three-phase inverter, one with standard sinusoidal pulse width modulation (SPWM) and the other with unified space vector PWM (USPWM), equipped with adaptive voltage control (AVC) and a load current observer (LCO). A photovoltaic (PV) source feeds into a controlled DC link, which in turn supplies a DC voltage to the inverter powering an RL load. From the simulation, it is clear that although SPWM meets the output standards, it has increased total harmonic distortion (THD) and slower transient response. Alternatively, using USPWM control decreased THD to 3.01% and made the system respond in 11 ms, compared to 22 ms before. The new method provides better efficiency and better power quality when used with dynamic loads.
Volume: 16
Issue: 4
Page: 2411-2418
Publish at: 2025-12-01

Fault diagnosis for inverter open circuit faults using DC-link signal and random forest-based technique

10.11591/ijpeds.v16.i4.pp2178-2185
Hoang-Giang Vu , Dang Toan Nguyen
Three-phase voltage source inverters based on insulated-gate bipolar transistors (IGBTs) are widely used in various industrial applications. Faults in IGBTs significantly affect the performance of the inverter and entire system. Robust and accurate fault detection are the key requirements of fault diagnosis methods. This paper explores a method for diagnosing power switch open circuit faults of a voltage source inverter based on machine learning algorithms. The diagnosis is performed in two steps, firstly the fault is detected by applying the Random Forest classifier algorithm with the DC-link signal. Next, the fault switch location is performed by additionally using the inverter output AC current signals. The diagnostic results based on simulation data show that the fault can be detected with maximum accuracy. Meanwhile, the accuracy in locating the fault switch is also significantly improved with the additional use of current signals measured at the DC-link. Potential application of electromagnetic field signal is also highlighted for the practical implementation of fault diagnosis.
Volume: 16
Issue: 4
Page: 2178-2185
Publish at: 2025-12-01

Adaptive ANFIS-based MPPT for PV-powered green ships with high gain SEPIC converter

10.11591/ijpeds.v16.i4.pp2768-2779
G. Jegadeeswari , Rohini Govindaraju , D. Balakumar , D. Lakshmi , S. Marisargunam , M. Batumalay , B. Kirubadurai
To align with global climate goals, the International Maritime Organization (IMO) has enforced strict measures to reduce greenhouse gas emissions from the shipping industry by promoting energy efficiency and cleaner propulsion methods. Ship engines remain major contributors to environmental pollution due to their dependence on fossil fuels and inefficient propulsion systems, highlighting the need for clean and sustainable alternatives. This study aims to design a renewable energy-based marine power system that effectively stores and utilizes solar energy, improving overall efficiency and reducing emissions for process innovation. A hybrid setup was developed using photovoltaic (PV) panels, batteries, and a bidirectional DC-DC converter to enable flexible power flow during both charging and discharging cycles. An adaptive neuro-fuzzy inference system (ANFIS)-based maximum power point tracking (MPPT) algorithm was employed alongside a SEPIC converter to enhance energy extraction from the PV system under dynamic conditions. The integrated system achieved a power extraction efficiency of 97.12%, confirming the effectiveness of the ANFIS-based MPPT strategy and showcasing the viability of intelligent renewable energy solutions in maritime applications.
Volume: 16
Issue: 4
Page: 2768-2779
Publish at: 2025-12-01

Design and implementation of digital logic for brushless DC motor control in electric vehicles

10.11591/ijpeds.v16.i4.pp2143-2155
Belwin J. Brearley , K. Regin Bose , K. Ganesh Kumar
In today's world, the rise in global warming is driving a shift towards electric mobility. The progress in battery technology and power electronic devices has facilitated the transition of vehicles from being powered by traditional internal combustion engines to electric motors. The types of motors utilized for propulsion include DC motors, three-phase induction motors, permanent magnet synchronous motors (PMSM), and brushless DC motors (BLDC). Among them, the BLDC motor, when paired with a suitable control algorithm, proves to be the most suitable option for electric vehicle applications. The existing control algorithms for BLD motors are quite complex. Therefore, this study presents the development of an innovative and simple digital control algorithm based on a combinational logic circuit to drive the BLDC motor under motoring and regenerative braking mode. The proposed control algorithm and its effectiveness are validated by simulating it using Xilinx & Proteus software and experimenting with the concept in hardware by utilizing a PIC microcontroller. The proposed control algorithm forms a cost-effective alternative for BLDC motor speed control.
Volume: 16
Issue: 4
Page: 2143-2155
Publish at: 2025-12-01

Designing, developing and analyzing of a rectangular-shaped patch antenna at 3.5 GHz for 5G applications at S band

10.11591/ijece.v15i6.pp5422-5432
Sukanto Halder , Md. Sohel Rana , Md Abdul Ahad , Md. Shehab Uddin Shahriar , Md. Abdulla Al Mamun , Md. Mominur Rahaman , Omer Faruk , Md. Eftiar Ahmed
This research study focuses on the design and analysis of two distinct patch antennas for 5G applications at 3.5 GHz. Rogers RT5880 served as the foundational material for antenna designs I and II. A 50 Ω feed line is utilized to supply both antennas. According to the calculations, Design I exhibits a reflection coefficient (S11) of -32.98 dB, a voltage standing wave ratio of 1.045, a gain of 7.81 dBi, an efficiency of 89.2%, and a surface current of 66.82 A/V. Design II has a reflection coefficient (S11) of 34.98 dB, voltage standing wave ratio (VSWR) of 1.036, gain of 8.78 dBi, efficiency of 89.87%, and surface current of 62.7 A/V. Among the two antenna designs, design II outperformed design I, and the results indicate that the antenna fulfilled the designated purpose. The novelties of the proposed paper are to design two different patch antennas using same materials and highlight the performance of the design parameters. Design II is proficient in supporting 5G services owing to its advantageous performance. In addition, S11 of the antenna is reduced to bring the VSWR value is close to 1. Also, improve gain, directivity and efficiency by bringing the antenna impedance matching close to 50 Ω.
Volume: 15
Issue: 6
Page: 5422-5432
Publish at: 2025-12-01

Exploring feature engineering and explainable AI for phishing website detection: a systematic literature review

10.11591/ijece.v15i6.pp5863-5878
Norah Alsuqayh , Abdulrahman Mirza , Areej Alhogail
Detecting phishing websites is a rapidly evolving field aimed at identifying and mitigating cyberattacks targeting individuals, organizations, and governments. Ongoing progress in artificial intelligence (AI) has the potential to revolutionize phishing detection by enhancing model accuracy and improving transparency through eXplainable AI (XAI). However, significant challenges remain, particularly in integrating feature engineering with XAI to address sophisticated phishing strategies including zero-day attacks, that evade traditional detection mechanisms. To overcome these challenges, this examines the impact of feature engineering and XAI in phishing detection, emphasizing their ability to enhance accuracy while providing interpretability. By integrating feature extraction with interpretable models, these techniques improve decision-making transparency and system robustness. This paper presents the first systematic literature review (SLR) focusing on the impact of feature engineering and XAI on state-of-the-art phishing detection approaches. Additionally, it identifies critical research gaps and challenges, including scalability issues, the evolution of phishing techniques, and balancing complexity with interpretability. The findings provide valuable academic insights while offering practical recommendations for developing accurate and interpretable phishing detection systems, aiding organizations in strengthening cybersecurity measures.
Volume: 15
Issue: 6
Page: 5863-5878
Publish at: 2025-12-01

Detection of breast cancer with ensemble learning using magnetic resonance imaging

10.11591/ijece.v15i6.pp5371-5379
Swati Nadkarni , Kevin Noronha
Despite notable progress in medicine along with technology, the deaths due to breast cancer are increasing steadily. This paper proposes a framework to aid the early detection of lesions in breast with magnetic resonance imaging (MRI). The work has been carried out using diffusion weighted imaging (DWI) and dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI). Data augmentation has been incorporated to enlarge the data set collected from a reputed hospital. Deep learning has been implemented using the ensemble of convolutional neural network (CNN). Amongst the individual CNN models, the you only look once (YOLO) CNN yielded the highest performance with an accuracy of 93.4%, sensitivity of 93.44%, specificity of 93.33%, and F1-score of 93.44%. Using Hungarian optimization, appropriate selection of individual CNN architectures to form the ensemble of CNN was possible. The ensemble model enhanced performance with 95.87% accuracy, 95.08% sensitivity, 96.67% specificity, and F1-score of 95.87%.
Volume: 15
Issue: 6
Page: 5371-5379
Publish at: 2025-12-01

Convolutional neural network-based hybrid beamforming design based on energy efficiency for mmWave M-MIMO systems

10.11591/ijece.v15i6.pp5443-5452
Hanane Ayad , Mohammed Yassine Bendimerad , Fethi Tarik Bendimerad
Millimeter-wave (mmWave) massive multiple-input multiple-output (M- MIMO) technology brings significant improvements in data transmission rates for communication systems. A key to the design of mmWave M-MIMO systems is beamforming techniques, which focus signals toward specific directions but rely on expensive, energy-intensive radio frequency (RF) chains. To address this issue, hybrid beamformers (HB) have been introduced as a partial solution, and deep learning (DL) has proven effective for HB design. However, previous works utilizing machine learning (ML) networks have primarily focused on the spectral efficiency (SE) metric for constructing HB. In this paper, we present a convolutional neural network (CNN) architecture whose loss function is defined to maximize energy efficiency (EE) directly. The network jointly learns analog and digital beamformers by evaluating EE (throughput per total power, including phase shifters, switches, digital-to-analog converters (DACs), and RF chains) and selecting the configuration that yields the highest EE. The CNN takes a channel matrix as input and outputs RF and baseband beamformer matrices. Simulation results validate the effectiveness of the proposed M-MIMO EE scheme, achieving significant EE improvements by optimizing hybrid precoding and reducing RF chain usage.
Volume: 15
Issue: 6
Page: 5443-5452
Publish at: 2025-12-01

Greenhouse gas reduction system for engines using electrolyte technology

10.11591/ijece.v15i6.pp5524-5534
Bopit Chainok , Boonthong Wasuri , Piyamas Chainok
This research focuses on developing a system to reduce greenhouse gas emissions in internal combustion vehicle engines using electrolyte technology and embedded programming on an electronic board via the OBI protocol. The main objectives are to create a prototype, apply it in real-world scenarios, evaluate its efficiency, and facilitate technology transfer. The system, designed to reduce greenhouse gases from vehicles, consists of a Bluetooth on-board diagnostics (OBD) scanner connected to the electronic control unit (ECU). This scanner transmits data to an embedded microcontroller through a Bluetooth module. The microcontroller, which includes software for controlling oxygen measurement and production, operates to decrease greenhouse gas emissions. The results show that the electronic device, IC ELM327, decodes OBD into RS232, processes the oxygen output from the exhaust pipe using embedded programming on the Arduino Uno-R3 microprocessor, and controls the oxygen production unit with electrolyte technology. The system adds 9.82% oxygen to the exhaust and reduces carbon monoxide by 21.04% and carbon dioxide by 13.86%. Additionally, the technology transfer received high satisfaction with a mean score of 4.61, indicating efficient technology dissemination.
Volume: 15
Issue: 6
Page: 5524-5534
Publish at: 2025-12-01

Smart wearable glove for enhanced human-robot interaction using multi-sensor fusion and machine learning

10.11591/ijece.v15i6.pp5162-5172
Nourdine Herbaz , Hassan El Idrissi , Hamza Sabir , Abdelmajid Badri
Hand gesture recognition (HGR) using flexible sensors (flex-sensor) and the MPU6050 sensor has proved to be a key area of research in human-machine interaction, with major applications in biasing, rehabilitation, and assisted robotics. This paper proposes a wearable intelligent glove designed to operate a robotics arm in real time, relying on multi-sensor fusion and machine learning methods to enhance the system's responsiveness and precision. The proposed system enables the intuitive reproduction of hand movements and precise control of the robotic arm. In the context of Industry 4.0 and internet of things (IoT), the classification of gestures is necessary for maintaining operational efficiency. To guarantee gesture recognition, data signals from the smart glove are collected and trained by a recurrent neural network (RNN), which achieves 98.67% accuracy for real-time classification of seven gestures. Beyond industrial applications, the wearable smart glove can be exploited in a recognized circuit of all systems, including rehabilitation exercises that involve recording the progression of muscular activity for the assessment of motor functions and serve as a tool for patient recovery.
Volume: 15
Issue: 6
Page: 5162-5172
Publish at: 2025-12-01

A new algorithm for quality-of-service improvement in mobile ad hoc networks

10.11591/ijece.v15i6.pp5466-5483
Hanafy M. Ali , Adel F. El-Kabbany , Yahia B. Hassan
The quality of service (QoS) in mobile ad hoc networks (MANETs) plays a crucial role in optimizing overall network resource utilization. MANET routing protocols, fundamental to QoS, demand adaptive and swift solutions for efficient path searching. In this context, our paper introduces a novel algorithm based on MANETs, employing a hybrid approach that combines ant colony optimization (ACO) with hybrid multipath quality of service ant (HMQAnt) routing protocols. Our algorithm emphasizes bandwidth optimization as a pivotal factor for providing effective paths. By incorporating bandwidth as a significant parameter in the MANETs algorithm, we aim to enhance its overall properties. The proposed routing protocol, focusing on bandwidth optimization, is anticipated to improve the delivery of total network traffic. Evaluation of the algorithm's performance is conducted through QoS metrics, which are overhead, end-to-end delay, and jitter, throughputs, utilizing a MATLAB simulator. Simulation results indicate that our proposed routing protocol holds a distinct advantage compared to ad hoc on-demand distance vector (AODV), destination- sequenced distance (DSDV), dynamic source routing (DSR), and hybrid ant colony optimization-based (ACO) routing protocol called (ANTMANET) algorithms.
Volume: 15
Issue: 6
Page: 5466-5483
Publish at: 2025-12-01

Support-centric PSO-based fuzzy MPPT tuning for photovoltaic systems under uniform conditions

10.11591/ijpeds.v16.i4.pp2792-2803
Amel Smaili , El-Ghalia Boudissa , M’hamed Bounekhla
Several conventional maximum power point tracking (MPPT) algorithms have been applied to harvest the optimal power of a photovoltaic (PV) system. However, the main drawbacks of these algorithms are their fluctuations around the maximum power point (MPP) and their dependence on climatic conditions variation. To overcome these issues, a fuzzy logic controller (FLC) is proposed, where the system performance depends strongly on the choice of membership functions (MFs). They are typically selected by trial and error, which may not always yield the best results. This paper seeks to enhance the efficiency of the traditional FLC method by using the particle swarm optimization (PSO) algorithm for optimizing the supports of the triangular MFs. The simulation was performed using MATLAB-Simulink environment using the "1Soltech 1STH-215-P" PV module and a single-ended primary-inductor converter (SEPIC) converter, under ideal environmental conditions of 25 °C and 1000 W/m². A comparison is established between PSO-optimized FLC and the standard FLC-based MPPT method, as well as with several other state-of-the-art approaches reported in related research. The simulation data present that the PSO-optimized FLC approach outperforms other algorithms.
Volume: 16
Issue: 4
Page: 2792-2803
Publish at: 2025-12-01
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